library(googleAnalyticsR)
library(keyring)
library(ggplot2)
library(tidyverse)
library(lubridate)
library(ggpubr)
#Get data for FY2017-2018 for sessions and goal completions by date
seventeen_eighteen_app_values <- read_csv("../inputs/seventeen_eighteen_apps.csv")
Parsed with column specification:
cols(
  date = col_date(format = ""),
  month = col_character(),
  year = col_double(),
  dayofWeekName = col_character(),
  day = col_character(),
  goal9Completions = col_double(),
  sessions = col_double(),
  goal3Completions = col_double(),
  goal5Completions = col_double(),
  goal12Completions = col_double()
)
summary(seventeen_eighteen_app_values)
#Plot application values against month 
ggplot(seventeen_eighteen_app_values) +
  aes(x = month, y = goal9Completions) +
  geom_col() +
  geom_hline(yintercept = 45, colour = "red")

#Plot app values against day of week
ggplot(seventeen_eighteen_app_values) +
  aes(x = dayofWeekName, y = goal9Completions) +
  geom_col() 
#Plot app values  and sessions over entire year
app_seventeen_plot <- ggplot() +
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = goal9Completions)) +
  labs(x = "Date", y = "Applications")

sessions_seventeen_plot <- ggplot() +
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = sessions)) +
  labs(x = "Date", y = "Sessions")

ggarrange(sessions_seventeen_plot, app_seventeen_plot,
          ncol = 1, nrow = 2)
ggsave("seventeen_sessions.jpg")
Saving 7.29 x 4.51 in image

#Plot app values and info session clicks over entire year
goal3_seventeen <- ggplot() +
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = goal3Completions, colour = "blue")) +
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = goal5Completions, colour = "red")) +
  labs(x = "Date", y = "Info Session Clicks") +
  theme(legend.position = "none")

goal9_seventeen<- 
  ggplot()+
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = goal9Completions)) +
  labs(x = "Date", y = "Applications")

ggarrange(goal3_seventeen, goal9_seventeen,
          ncol = 1, nrow = 2)
ggsave("seventeen_goal35.jpg")
Saving 7.29 x 4.51 in image

#Plot app values  and info packs over entire year
info_packs_seventeen_plot <- ggplot() +
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = goal12Completions, colour = "blue")) +
  labs(x = "Date", y = "Info Pack Requests") +
   theme(legend.position = "none")

ggarrange(info_packs_seventeen_plot, goal9_seventeen,
          ncol = 1, nrow = 2)
ggsave("seventeen_goal12.jpg")
Saving 7.29 x 4.51 in image

#Get correlations between metrics and app values
cor(seventeen_eighteen_app_values$goal9Completions, seventeen_eighteen_app_values$sessions)
[1] 0.4036846
cor(seventeen_eighteen_app_values$goal9Completions, seventeen_eighteen_app_values$goal3Completions)
[1] 0.2278833
cor(seventeen_eighteen_app_values$goal9Completions, seventeen_eighteen_app_values$goal5Completions)
[1] 0.07772192
cor(seventeen_eighteen_app_values$goal9Completions, seventeen_eighteen_app_values$goal12Completions)
[1] 0.03673292

Strongest correlation between sessions and app values but weak - info packs not really correlated, too weak

#Get data for FY2018/2019
eighteen_nineteen_app_values <-  read_csv("../inputs/eighteen_nineteen_apps.csv")
Parsed with column specification:
cols(
  date = col_date(format = ""),
  month = col_character(),
  year = col_double(),
  dayofWeekName = col_character(),
  day = col_character(),
  goal9Completions = col_double(),
  sessions = col_double(),
  goal3Completions = col_double(),
  goal5Completions = col_double(),
  goal12Completions = col_double()
)
summary(eighteen_nineteen_app_values)
      date               month                year      dayofWeekName     
 Min.   :2018-04-01   Length:365         Min.   :2018   Length:365        
 1st Qu.:2018-07-01   Class :character   1st Qu.:2018   Class :character  
 Median :2018-09-30   Mode  :character   Median :2018   Mode  :character  
 Mean   :2018-09-30                      Mean   :2018                     
 3rd Qu.:2018-12-30                      3rd Qu.:2018                     
 Max.   :2019-03-31                      Max.   :2019                     
     day            goal9Completions    sessions     goal3Completions goal5Completions
 Length:365         Min.   :0.000    Min.   : 90.0   Min.   : 0.000   Min.   :0.00    
 Class :character   1st Qu.:0.000    1st Qu.:227.0   1st Qu.: 0.000   1st Qu.:0.00    
 Mode  :character   Median :1.000    Median :314.0   Median : 1.000   Median :2.00    
                    Mean   :1.115    Mean   :305.4   Mean   : 1.756   Mean   :2.06    
                    3rd Qu.:2.000    3rd Qu.:371.0   3rd Qu.: 3.000   3rd Qu.:3.00    
                    Max.   :8.000    Max.   :697.0   Max.   :15.000   Max.   :9.00    
 goal12Completions
 Min.   :0.000    
 1st Qu.:1.000    
 Median :2.000    
 Mean   :2.233    
 3rd Qu.:3.000    
 Max.   :8.000    
#Create data frame with both year's data
all_years_app_values <- 
rbind(seventeen_eighteen_app_values, eighteen_nineteen_app_values)

all_years_app_values <- 
all_years_app_values %>%
  mutate(FY = case_when(
    month %in% c("04", "05", "06", "07", "08", "09", "10", "11", "12") & year == "2017" ~ "2017/18",
    month %in% c("01", "02", "03") & year == "2018" ~ "2017/18",
    month %in% c("04", "05", "06", "07", "08", "09", "10", "11", "12") & year == "2018" ~ "2018/19",
    month %in% c("01", "02", "03") & year == "2019" ~ "2018/19",
  ))
sum(all_years_app_values$goal9Completions)
[1] 919
#Plot app values for each month and compare to last year
all_years_app_values %>%
  group_by(month, FY) %>%
  summarise(total_apps = sum(goal9Completions)) %>%
  ggplot() +
  geom_col(aes(x = month, y = total_apps, fill = FY), position = "dodge") +
  scale_fill_manual(values = c("violetred3", "springgreen3")) +
  geom_hline(yintercept = 45) +
  labs(title = "Applications per Month", x = "Month", y = "Number of Applications") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("apps_month.jpg")
Saving 7.29 x 4.51 in image

#Plot apps by day of the week and compare to last year
all_years_app_values %>%
  group_by(dayofWeekName, FY) %>%
  summarise(total_apps = sum(goal9Completions)) %>%
  ggplot() +
  geom_col(aes(x = reorder(dayofWeekName, total_apps), y = total_apps, fill = FY), position = "dodge") +
  scale_fill_manual(values = c("violetred3", "springgreen3")) +
  labs(title = "Applications per Week Day", x = "Day", y = "Number of Applications") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("apps_day.jpg")
Saving 7.29 x 4.51 in image

sum(seventeen_eighteen_app_values$goal9Completions)
[1] 512
sum(eighteen_nineteen_app_values$goal9Completions)
[1] 407
#Plot apps by day of the month and compare to last year
all_years_app_values %>%
  group_by(day, FY) %>%
  summarise(total_apps = sum(goal9Completions)) %>%
  ggplot() +
  geom_col(aes(x = day, y = total_apps, fill = FY), position = "dodge") +
  scale_fill_manual(values = c("violetred3", "springgreen3")) +
  labs(title = "Applications per Month Day", x = "Day of Month", y = "Number of Applications") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("apps_month_day.jpg")
Saving 7.29 x 4.51 in image

#Plot sessions vs apps for FY2018/19
app_eighteen_plot <- ggplot() +
  geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = goal9Completions))  +
  labs(x = "Date", y = "Applications")

sessions_eighteen_plot <- ggplot() +
  geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = sessions)) +
  labs(x = "Date", y = "Sessions")

ggarrange(sessions_eighteen_plot, app_eighteen_plot,
          ncol = 1, nrow = 2)
ggsave("eighteen_sessions.jpg")
Saving 7.29 x 4.51 in image

#Plot app values and info session clicks over entire year
info_clicks_eighteen_plot <- ggplot() +
  geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = goal3Completions, colour = "blue")) +
  geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = goal5Completions, colour = "red")) +
  labs(x = "Date", y = "Info Session Clicks") +
  theme(legend.position = "none")

goal9_eighteen_plot <- 
ggplot() +
   geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = goal9Completions)) +
labs(x = "Date", y = "Applications")

ggarrange(info_clicks_eighteen_plot, goal9_eighteen_plot,
          ncol = 1, nrow = 2)
ggsave("eighteen_goal35.jpg")
Saving 7.29 x 4.51 in image

#Plot app values  and info packs over entire year
info_packs_eighteen_plot <- ggplot() +
  geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = goal12Completions, colour = "red")) +
  labs(x = "Date", y = "Info Pack Requests") +
  theme(legend.position = "none")

ggarrange(info_packs_eighteen_plot, goal9_eighteen_plot,
          ncol = 1, nrow = 2)

ggsave("eighteen_goal12.jpg")
Saving 7.29 x 4.51 in image

#Get correlations between metrics and app values
cor(eighteen_nineteen_app_values$goal9Completions, eighteen_nineteen_app_values$sessions)
[1] 0.3057772
cor(eighteen_nineteen_app_values$goal9Completions, eighteen_nineteen_app_values$goal3Completions)
[1] 0.1224082
cor(eighteen_nineteen_app_values$goal9Completions, eighteen_nineteen_app_values$goal5Completions)
[1] 0.3110095
cor(eighteen_nineteen_app_values$goal9Completions, eighteen_nineteen_app_values$goal12Completions)
[1] 0.1509175
#Compare applications for each year
ggplot() +
  geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = goal9Completions), colour = "violetred3") +
  geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = goal9Completions), colour = "springgreen3") +
  labs(x = "Date", y = "Number of Applications") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("apps_2years.jpg")
Saving 7.29 x 4.51 in image

#Compare sessions for each year
ggplot() +
  geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = sessions), colour = "violetred3") +
  geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = sessions), colour = "springgreen3") +
  labs(x = "Date", y = "Number of Sessions") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("session_2years.jpg")
Saving 7.29 x 4.51 in image

#Compare info clicks for each year
ggplot() +
  geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = goal3Completions), colour = "red") +
  geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = goal5Completions), colour = "blue") +
  geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = goal3Completions), colour = "yellow") +
  geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = goal5Completions), colour = "green") +
  labs(x = "Date", y = "Number of Info Session Clicks") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("goal35_2years.jpg")
Saving 7.29 x 4.51 in image

#Compare info packs by year
ggplot() +
  geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = goal12Completions), colour = "violetred3") +
  geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = goal12Completions), colour = "springgreen3") +
  labs(x = "Date", y = "Number of Info Pack Requests") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("goal12_2years.jpg")
Saving 7.29 x 4.51 in image

#Plot apps for 2017/18
all_years_app_values %>%
  filter(FY == "2017/18")  %>%
  ggplot() +
  aes(x = date, y = goal9Completions) +
  geom_line()

#Plot apps for 2018/19
all_years_app_values %>%
  filter(FY == "2018/19")  %>%
  ggplot() +
  aes(x = date, y = goal9Completions) +
  geom_line()

---
title: "Part_1_notebook"
output: html_notebook
---

```{r}
library(googleAnalyticsR)
library(keyring)
library(ggplot2)
library(tidyverse)
library(lubridate)
library(ggpubr)
```

```{r}
#Get data for FY2017-2018 for sessions and goal completions by date
seventeen_eighteen_app_values <- read_csv("../inputs/seventeen_eighteen_apps.csv")
```

```{r}
summary(seventeen_eighteen_app_values)
```

```{r}
#Plot application values against month 
ggplot(seventeen_eighteen_app_values) +
  aes(x = month, y = goal9Completions) +
  geom_col() +
  geom_hline(yintercept = 45, colour = "red")
```

```{r}
#Plot app values against day of week
ggplot(seventeen_eighteen_app_values) +
  aes(x = dayofWeekName, y = goal9Completions) +
  geom_col() 
```

```{r}
#Plot app values  and sessions over entire year
app_seventeen_plot <- ggplot() +
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = goal9Completions)) +
  labs(x = "Date", y = "Applications")

sessions_seventeen_plot <- ggplot() +
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = sessions)) +
  labs(x = "Date", y = "Sessions")

ggarrange(sessions_seventeen_plot, app_seventeen_plot,
          ncol = 1, nrow = 2)
ggsave("seventeen_sessions.jpg")
```


```{r}
#Plot app values and info session clicks over entire year
goal3_seventeen <- ggplot() +
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = goal3Completions, colour = "blue")) +
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = goal5Completions, colour = "red")) +
  labs(x = "Date", y = "Info Session Clicks") +
  theme(legend.position = "none")

goal9_seventeen<- 
  ggplot()+
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = goal9Completions)) +
  labs(x = "Date", y = "Applications")

ggarrange(goal3_seventeen, goal9_seventeen,
          ncol = 1, nrow = 2)
ggsave("seventeen_goal35.jpg")
```

```{r}
#Plot app values  and info packs over entire year
info_packs_seventeen_plot <- ggplot() +
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = goal12Completions, colour = "blue")) +
  labs(x = "Date", y = "Info Pack Requests") +
   theme(legend.position = "none")

ggarrange(info_packs_seventeen_plot, goal9_seventeen,
          ncol = 1, nrow = 2)
ggsave("seventeen_goal12.jpg")
```

```{r}
#Get correlations between metrics and app values
cor(seventeen_eighteen_app_values$goal9Completions, seventeen_eighteen_app_values$sessions)
cor(seventeen_eighteen_app_values$goal9Completions, seventeen_eighteen_app_values$goal3Completions)
cor(seventeen_eighteen_app_values$goal9Completions, seventeen_eighteen_app_values$goal5Completions)
cor(seventeen_eighteen_app_values$goal9Completions, seventeen_eighteen_app_values$goal12Completions)
```
Strongest correlation between sessions and app values but weak - info packs not
really correlated, too weak


```{r}
#Get data for FY2018/2019
eighteen_nineteen_app_values <-  read_csv("../inputs/eighteen_nineteen_apps.csv")
```

```{r}
summary(eighteen_nineteen_app_values)
```

```{r}
#Create data frame with both year's data
all_years_app_values <- 
rbind(seventeen_eighteen_app_values, eighteen_nineteen_app_values)

all_years_app_values <- 
all_years_app_values %>%
  mutate(FY = case_when(
    month %in% c("04", "05", "06", "07", "08", "09", "10", "11", "12") & year == "2017" ~ "2017/18",
    month %in% c("01", "02", "03") & year == "2018" ~ "2017/18",
    month %in% c("04", "05", "06", "07", "08", "09", "10", "11", "12") & year == "2018" ~ "2018/19",
    month %in% c("01", "02", "03") & year == "2019" ~ "2018/19",
  ))
```

```{r}
sum(all_years_app_values$goal9Completions)
```



```{r}
#Plot app values for each month and compare to last year
all_years_app_values %>%
  group_by(month, FY) %>%
  summarise(total_apps = sum(goal9Completions)) %>%
  ggplot() +
  geom_col(aes(x = month, y = total_apps, fill = FY), position = "dodge") +
  scale_fill_manual(values = c("violetred3", "springgreen3")) +
  geom_hline(yintercept = 45) +
  labs(title = "Applications per Month", x = "Month", y = "Number of Applications") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("apps_month.jpg")
```

```{r}
#Plot apps by day of the week and compare to last year
all_years_app_values %>%
  group_by(dayofWeekName, FY) %>%
  summarise(total_apps = sum(goal9Completions)) %>%
  ggplot() +
  geom_col(aes(x = reorder(dayofWeekName, total_apps), y = total_apps, fill = FY), position = "dodge") +
  scale_fill_manual(values = c("violetred3", "springgreen3")) +
  labs(title = "Applications per Week Day", x = "Day", y = "Number of Applications") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("apps_day.jpg")
```

```{r}
sum(seventeen_eighteen_app_values$goal9Completions)
sum(eighteen_nineteen_app_values$goal9Completions)
```

```{r}
#Plot apps by day of the month and compare to last year
all_years_app_values %>%
  group_by(day, FY) %>%
  summarise(total_apps = sum(goal9Completions)) %>%
  ggplot() +
  geom_col(aes(x = day, y = total_apps, fill = FY), position = "dodge") +
  scale_fill_manual(values = c("violetred3", "springgreen3")) +
  labs(title = "Applications per Month Day", x = "Day of Month", y = "Number of Applications") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("apps_month_day.jpg")
```

```{r}
#Plot sessions vs apps for FY2018/19
app_eighteen_plot <- ggplot() +
  geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = goal9Completions))  +
  labs(x = "Date", y = "Applications")

sessions_eighteen_plot <- ggplot() +
  geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = sessions)) +
  labs(x = "Date", y = "Sessions")

ggarrange(sessions_eighteen_plot, app_eighteen_plot,
          ncol = 1, nrow = 2)
ggsave("eighteen_sessions.jpg")
```

```{r}
#Plot app values and info session clicks over entire year
info_clicks_eighteen_plot <- ggplot() +
  geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = goal3Completions, colour = "blue")) +
  geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = goal5Completions, colour = "red")) +
  labs(x = "Date", y = "Info Session Clicks") +
  theme(legend.position = "none")

goal9_eighteen_plot <- 
ggplot() +
   geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = goal9Completions)) +
labs(x = "Date", y = "Applications")

ggarrange(info_clicks_eighteen_plot, goal9_eighteen_plot,
          ncol = 1, nrow = 2)
ggsave("eighteen_goal35.jpg")
```

```{r}
#Plot app values  and info packs over entire year
info_packs_eighteen_plot <- ggplot() +
  geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = goal12Completions, colour = "red")) +
  labs(x = "Date", y = "Info Pack Requests") +
  theme(legend.position = "none")

ggarrange(info_packs_eighteen_plot, goal9_eighteen_plot,
          ncol = 1, nrow = 2)

ggsave("eighteen_goal12.jpg")
```

```{r}
#Get correlations between metrics and app values
cor(eighteen_nineteen_app_values$goal9Completions, eighteen_nineteen_app_values$sessions)
cor(eighteen_nineteen_app_values$goal9Completions, eighteen_nineteen_app_values$goal3Completions)
cor(eighteen_nineteen_app_values$goal9Completions, eighteen_nineteen_app_values$goal5Completions)
cor(eighteen_nineteen_app_values$goal9Completions, eighteen_nineteen_app_values$goal12Completions)
```

```{r}
#Compare applications for each year
ggplot() +
  geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = goal9Completions), colour = "violetred3") +
  geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = goal9Completions), colour = "springgreen3") +
  labs(x = "Date", y = "Number of Applications") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("apps_2years.jpg")
```


```{r}
#Compare sessions for each year
ggplot() +
  geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = sessions), colour = "violetred3") +
  geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = sessions), colour = "springgreen3") +
  labs(x = "Date", y = "Number of Sessions") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("session_2years.jpg")
```

```{r}
#Compare info clicks for each year
ggplot() +
  geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = goal3Completions), colour = "red") +
  geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = goal5Completions), colour = "blue") +
  geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = goal3Completions), colour = "yellow") +
  geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = goal5Completions), colour = "green") +
  labs(x = "Date", y = "Number of Info Session Clicks") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("goal35_2years.jpg")
```

```{r}
#Compare info packs by year
ggplot() +
  geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = goal12Completions), colour = "violetred3") +
  geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = goal12Completions), colour = "springgreen3") +
  labs(x = "Date", y = "Number of Info Pack Requests") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("goal12_2years.jpg")
```

```{r}
#Plot apps for 2017/18
all_years_app_values %>%
  filter(FY == "2017/18")  %>%
  ggplot() +
  aes(x = date, y = goal9Completions) +
  geom_line()
```

```{r}
#Plot apps for 2018/19
all_years_app_values %>%
  filter(FY == "2018/19")  %>%
  ggplot() +
  aes(x = date, y = goal9Completions) +
  geom_line()
```






















